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This article reviews the off-label prescription of quetiapine in the treatment of a broad range of psychiatric disorders including obsessive-compulsive disorder, post-traumatic stress disorder, personality disorder, substance abuse, bipolar disorder (now US FDA approved), anxiety and depression. The article highlights the primary reliance on selective serotonin reuptake inhibitors (SSRIs) in the treatment of these disorders (cf bipolar disorder) and the high percentage of patients (30-60%) that do not respond to SSRIs. The studies suggest that low-dose quetiapine shows good tolerability and efficacy in patients diagnosed with these disorders, particularly in the case of treatment-resistant patients that do not respond to primary treatments including SSRIs and cognitive-behavioral therapy. Quetiapine generally appears to be very effective in trauma-related conditions by improving autonomic stability, and decreasing the stress and anxiety response that arises due to specific fears or triggers. Quetiapine also appears to be particularly useful for normalizing obsessions and compulsions, and improving low mood, irritability and aggressiveness. A greater understanding of the pharmacology of drug alternatives and the neurobiology of psychiatric disorders is required to permit a more personalized medicine approach.

Despite high rates of prescription, little is known about the long-term consequences of stimulant medication therapy for attention-deficit hyperactivity disorder (ADHD) sufferers. Historically, the clinical use of stimulants for ADHD has been based on trial and error before optimal therapy is reached. Concurrently, scientific research on the mechanism of action of stimulants has influenced neurobiological models of ADHD, but has not always informed their prescription. Whilst the two main stimulant types (methylphenidate and dexamphetamine) have numerous similarities, they also differ (slightly) in mechanism and possibly individual response. A further issue relates to differences in cost and availability compounded by the expectation for stimulants to be effective in ameliorating a broad spectrum of ADHD-related symptoms. Thus, there is an increasing need for treating clinicians to prescribe not only the most effective drug, but also the most appropriate dose with the associated release mechanism and schedule for each ADHD patient presented. In this regard, the field is witnessing an emergence of the personalized medicine approach to ADHD, in which treatment decisions are tailored to each individual. This shift requires a new approach to research into treatment response prediction. Given the heterogeneity of ADHD, a profile of information may be required to capture the most sensitive predictors of treatment response in individuals. These profiles will also benefit from the integration of data from clinical rating scales with more direct measures of cognition and brain function. In conclusion, there is a need to establish a more robust normative framework as the baseline for treatment, as well as diagnostic decisions, and as discussed, the growth of integrated neuroscience databases will be important in this regard.

A framework for investigating information processing in cortico-thalamocortical (cortico-TC) networks is presented, that in part can be used to model and interpret individual changes in electroencephalographic spectra and event-related potentials such as those from the Brain Resource International Database. Scientific work covering neurophysiology, TC firing modes, and TC models are explored in the framework to explain how the brain might process complex information in a multistage process. It is proposed that the thalamus and the cortico-TC system have unique ionic properties and transmission delays (in humans), which are suited to the function of taking "snapshots" or samples of complex environmental stimuli, rather than continuous data streams. This leads to careful and sequential coordination of stimulus and response processes, and increases the probability of information transfer and the resulting information complexity in higher cortical regions. Given the scope of this framework, the multidimensional and standardized Brain Resource International Database provides a pertinent set of measures for both testing hypotheses generated from the model, and for fitting the model to experimental data to investigate mechanisms underlying information processing.

Objective To infer the neural mechanisms underlying tonic transitions in the electroencephalogram (EEG) in 11 adolescents diagnosed with attention deficit hyperactivity disorder (ADHD) before and after treatment with stimulant medication. Methods A biophysical model was used to analyse electroencephalographic (EEG) measures of tonic brain activity at multiple scalp sites before and after treatment with medication. Results It was observed that stimulants had the affect of significantly reducing the parameter controlling activation in the intrathalamic pathway involving the thalamic reticular nucleus (TRN) and the parameter controlling excitatory cortical activity. The effect of stimulant medication was also found to be preferentially localized within subcortical nuclei projecting towards frontal and central scalp sites. Conclusions It is suggested that the action of stimulant medication occurs via suppression of the locus coeruleus, which in turn reduces stimulation of the TRN, and improves cortical arousal. The effects localized to frontal and central sites are consistent with the occurrence of frontal delta–theta EEG abnormalities in ADHD, and existing theories of hypoarousal. Significance To our knowledge, this is the first study where a detailed biophysical model of the brain has been used to estimate changes in neurophysiological parameters underlying the effects of stimulant medication in ADHD.

Psychophysiological theories characterize Attention Deficit Hyperactivity Disorder (ADHD) in terms of cortical hypoarousal and a lack of inhibition of irrelevant sensory input, drawing on evidence of abnormal electroencephalographic (EEG) delta-theta activity. To investigate the mechanisms underlying this disorder a biophysical model of the cortex was used to fit and replicate the EEGs from 54 ADHD adolescents and their control subjects. The EEG abnormalities in ADHD were accounted for by the model's neurophysiological parameters as follows: (i) dendritic response times were increased, (ii) intrathalamic activity involving the thalamic reticular nucleus (TRN) was increased, consistent with enhanced delta-theta activity, and (iii) intracortical activity was increased, consistent with slow wave (

It is shown that new model‐based electroencephalographic (EEG) methods can quantify neurophysiologic parameters that underlie EEG generation in ways that are complementary to and consistent with standard physiologic techniques. This is done by isolating parameter ranges that give good matches between model predictions and a variety of experimental EEG‐related phenomena simultaneously. Resulting constraints range from the submicrometer synaptic level to length scales of tens of centimeters, and from timescales of around 1 ms to 1 s or more, and are found to be consistent with independent physiologic and anatomic measures. In the process, a new method of obtaining model parameters from the data is developed, including a Monte Carlo implementation for use when not all input data are available. Overall, the approaches used are complementary to other methods, constraining allowable parameter ranges in different ways and leading to much tighter constraints overall. EEG methods often provide the most restrictive individual constraints. This approach opens a new, noninvasive window on quantitative brain analysis, with the ability to monitor temporal changes, and the potential to map spatial variations. Unlike traditional phenomenologic quantitative EEG measures, the methods proposed here are based explicitly on physiology and anatomy. Hum. Brain Mapping 23:53–72, 2004. © 2004 Wiley‐Liss, Inc.

Attentional impairment in depression is a cardinal feature of depression and has been proposed as a candidate endophenotype for major depressive disorder. Event‐related potentials (ERPs) elicited by oddball signal detection tasks provide objective markers of selective stimulus processing, and are pertinent endophenotypic markers for depression. While previous studies have sought to determine objective markers for attentional impairment in depression, evidence is inconsistent and may involve heterogeneity in relatively small samples. Here, we brought together oddball ERP recording with source localization of neural correlates of selective attention in outpatients with major depressive disorder (MDD; n = 78) and participants with depressed mood (PDM; n = 127) relative to healthy controls (CTL; n = 116). The key finding was a dimensional exaggeration of the P200 (140–270 ms) to both target (signal) and non‐target (noise) stimuli, most pronounced in MDD, followed by PDM, relative to CTL. This exaggeration was coupled with slower and more variable response times, suggesting that neural systems are attempting to compensate for a difficulty in discriminating signal from noise. P200 alterations were localised to limbic (hippocampal), temporal and ventral prefrontal regions, key components of the signal detection network. A subsequent reduction and delay in the P300 was also revealed for MDD indicating that the pronounced lack of discrimination in clinical depression may also lead to impaired stimulus evaluation. This P200 increase in depression could provide a potential mechanism for the attentional impairment frequently observed in depression and consequent alterations in the P300 may differentiate clinically significant depression. Hum Brain Mapp, 2009. © 2008 Wiley‐Liss, Inc.

In this review, we draw on literature from both animal and human neurophysiological studies to consider the neurochemical mechanisms underlying attention-deficit/ hyperactivity disorder (ADHD). Psychophysiological and neuropsychological research is used to propose possible etiological endophenotypes of ADHD. These are conceptualized as patients with distinct cortical-arousal, information-processing or maturational abnormalities, or a combination thereof, and how the endophenotypes can be used to help drug development and optimize treatment and management. To illustrate, the paper focuses on neuro- and psychophysiological evidence that suggests cholinergic mechanisms may underlie specific information-processing abnormalities that occur in ADHD. The clinical implications for a cholinergic hypothesis of ADHD are considered, along with its possible implications for treatment and pharmacological development.

Aims: To examine how general (e.g., memory, attention) and social (emotional and interpersonal processes) cognition relate to measures of brain function and structure. Methods: PCA was used to identify general and social cognitive factors from the Brain Resource International Database in 1,316 subjects. The identified factors were correlated with each subject's corresponding brain structure (MRI) and function (EEG/ERP) data. Results: Seven core cognitive factors were identified for general and three for social. General cognition was correlated with global grey matter, while social cognition was negatively correlated with grey matter in fronto-temporal-somatosensory regions. Executive function, information processing speed and verbal memory performance were correlated with delta-theta qEEG, while most general cognitive factors negatively correlated with beta qEEG. Faster information processing speed was correlated with alpha qEEG. Executive function and information processing speed was correlated with negative-going ERP amplitude and slower ERP latency at frontal sites, but at posterior sites negative correlations were found. Discussion: In contrast to general cognition, social cognition is identified by different functional (automated) activity and more localized neural structures. Only general cognition, requiring more effortful, controlled processing is related to brain function measures, particularly in frontal cortices. Integrative Significance: Recording measures from multiple modalities including MRI, EEG/ERP, social and general cognition within the same subject provides a method of brain profiling for use in cognitive-neurotherapy and pharmacological studies.

Aims: Increasing age is the strongest risk factor for Alzheimer's disease (AD). Yet, departure from normal age-related decline for established markers of AD including memory, cognitive decline and brain function deficits, has not been quantified. Methods: We examined the cross-sectional estimates of the "rate of decline" in cognitive performance and psychophysiological measures of brain function over age in AD, preclinical (subjective memory complaint-SMC, Mild Cognitive Impairment-MCI) and healthy groups. Correlations between memory performance and indices of brain function were also conducted. Results: The rate of cognitive decline increased between groups: AD showed advanced decline, and SMC/MCI groups represented intermediate stages of decline relative to normal aging expectations. In AD, advanced EEG alterations (excessive slow-wave/reduced fast-wave EEG, decreased working memory P450 component) were observed over age, which were coupled with memory decline. By contrast, MCI group showed less severe cognitive changes but specific decreases in the working memory N300 component and slow-wave (delta) EEG, associated with decline in memory. Discussion and Integrative Significance: While the cognitive data suggests a continuum of deterioration associated with increasing symptom severity across groups, integration with brain function measures points to possible distinct compensatory strategies in MCI and AD groups. An integrative approach offers the potential for objective markers of the critical turning point, with age as a potential factor, from mild memory problems to disease.

Item does not contain fulltext New treatments for Alzheimer's disease require early detection of cognitive decline. Most studies seeking to identify markers of early cognitive decline have focused on a limited number of measures. We sought to establish the profile of brain function measures which best define early neuropsychological decline. We compared subjects with subjective memory complaints to normative controls on a wide range of EEG derived measures, including a new measure of event-related spatio-temporal waves and biophysical modeling, which derives anatomical and physiological parameters based on subject's EEG measurements. Measures that distinguished the groups were then related to cognitive performance on a variety of learning and executive function tasks. The EEG measures include standard power measures, peak alpha frequency, EEG desynchronization to eyes-opening, and global phase synchrony. The most prominent differences in subjective memory complaint subjects were elevated alpha power and an increased number of spatio-temporal wave events. Higher alpha power and changes in wave activity related most strongly to a decline in verbal memory performance in subjects with subjective memory complaints, and also declines in maze performance and working memory reaction time. Interestingly, higher alpha power and wave activity were correlated with improved performance in reverse digit span in the subjective memory complaint group. The modeling results suggest that differences in the subjective memory complaint subjects were due to a decrease in cortical and thalamic inhibitory gains and slowed dendritic time-constants. The complementary profile that emerges from the variety of measures and analyses points to a nonlinear progression in electrophysiological changes from early neuropsychological decline to late-stage dementia, and electrophysiological changes in subjective memory complaint that vary in their relationships to a range of memory-related tasks. 26 p.

A recently developed quantitative model of cortical activity is used that permits data comparison with experiment using a quantitative and standardized means. The model incorporates properties of neurophysiology including axonal transmission delays, synaptodendritic rates, range-dependent connectivities, excitatory and inhibitory neural populations, and intrathalamic, intracortical, corticocortical and corticothalamic pathways. This study tests the ability of the model to determine unique physiological properties in a number of different data sets varying in mean age and pathology. The model is used to fit individual electroencephalographic (EEG) spectra from post-traumatic stress disorder (PTSD), schizophrenia, first episode schizophrenia (FESz), attention deficit hyperactivity disorder (ADHD), and their age/sex matched controls. The results demonstrate that the model is able to distinguish each group in terms of a unique cluster of abnormal parameter deviations. The abnormal physiology inferred from these parameters is also consistent with known theoretical and experimental findings from each disorder. The model is also found to be sensitive to the effects of medication in the schizophrenia and FESz group, further supporting the validity of the model.

This paper presents the results from using electroencephalographic (EEG) data to estimate the values of key neurophysiological parameters using a detailed biophysical model of brain activity. The model incorporates spatial and temporal aspects of cortical function including axonal transmission delays, synapto-dendritic rates, range-dependent connectivities, excitatory and inhibitory neural populations, and intrathalamic, intracortical, corticocortical and corticothalamic pathways. Parameter estimates were obtained by fitting the model's theoretical spectrum to EEG spectra from each of 100 healthy human subjects. Statistical analysis was used to infer significant parameter variations occurring between eyes-closed and eyes-open states, and a correlation matrix was used to investigate links between the parameter variations and traditional measures of quantitative EEG (qEEG). Accurate fits to all experimental spectra were observed, and both inter-subject and between-state variability were accounted for by the variance in the fitted biophysical parameters, which were in turn consistent with known independent experimental and theoretical estimates. These values thus provide physiological information regarding the state. transitions (eyes-closed vs. eyes-open) and phenomena including cortical idling and alpha desynchronization. The parameters are also consistent with traditional qEEG, but are more informative, since they provide links to underlying physiological processes. To our knowledge, this is the first study where a detailed biophysical model of the brain is used to estimate neurophysiological parameters underlying the transitions in a broad range (0.25-50 Hz) of EEG spectra obtained from a large set of human data.

A recent neurophysical model of brain electrical activity is outlined and applied to EEG phenomena. It incorporates single-neuron physiology and the large-scale anatomy of corticocortical and corticothalamic pathways, including synaptic strengths, dendritic propagation, nonlinear firing responses, and axonal conduction. Small perturbations from steady states account for observed EEGs as functions of arousal. Evoked response potentials (ERPs), correlation, and coherence functions are also reproduced. Feedback via thalamic nuclei is critical in determining the forms of these quantities, the transition between sleep and waking, and stability against seizures. Many disorders correspond to significant changes in EEGs, which can potentially be quantified in terms of the underlying physiology using this theory. In the nonlinear regime, limit cycles are often seen, including a regime in which they have the characteristic petit mal 3 Hz spike-and-wave form.

Simulation of electrocortical activity requires (a) determination of the most crucial features to be modelled, (b) specification of state equations with parameters that can be determined against independent measurements, and (c) explanation of electrical events in the brain at several scales. We report our attempts to address these problems, and show that mutually consistent explanations, and simulation of experimental data can be achieved for cortical gamma activity, synchronous oscillation, and the main features of the EEG power spectrum including the cerebral rhythms and evoked potentials. These simulations include consideration of dendritic and synaptic dynamics, AMPA, NMDA, and GABA receptors, and intracortical and cortical/subcortical interactions. We speculate on the way in which Hebbian learning and intrinsic reinforcement processes might complement the brain dynamics thus explained, to produce elementary cognitive operations.

Links between electroencephalograms (EEGs) and underlying aspects of neurophysiology and anatomy are poorly understood. Here a nonlinear continuum model of large-scale brain electrical activity is used to analyze arousal states and their stability and nonlinear dynamics for physiologically realistic parameters. A simple ordered arousal sequence in a reduced parameter space is inferred and found to be consistent with experimentally determined parameters of waking states. Instabilities arise at spectral peaks of the major clinically observed EEG rhythms---mainly slow wave, delta, theta, alpha, and sleep spindle---with each instability zone lying near its most common experimental precursor arousal states in the reduced space. Theta, alpha, and spindle instabilities evolve toward low-dimensional nonlinear limit cycles that correspond closely to EEGs of petit mal seizures for theta instability, and grand mal seizures for the other types. Nonlinear stimulus-induced entrainment and seizures are also seen, EEG spectra and potentials evoked by stimuli are reproduced, and numerous other points of experimental agreement are found. Inverse modeling enables physiological parameters underlying observed EEGs to be determined by a new, noninvasive route. This model thus provides a single, powerful framework for quantitative understanding of a wide variety of brain phenomena.

A recent neurophysical model of propagation of electrical waves in the cortex is extended to include a physiologically motivated subcortical feedback loop via the thalamus. The electroencephalographic spectrum when the system is driven by white noise is then calculated analytically in terms of physiological parameters, including the effects of filtering of signals by the cerebrospinal fluid, skull, and scalp. The spectral power at low frequencies is found to vary as f(-1) when awake and f(-3) when asleep, with a breakpoint to a steeper power-law tail at frequencies above about 20 Hz in both cases; the f(-1) range concurs with recent magnetoencephalographic observations of such a regime. Parameter sensitivities are explored, enabling a model with fewer free parameters to be proposed, and showing that spectra predicted for physiologically reasonable parameter values strongly resemble those observed in the laboratory. Alpha and beta peaks seen near 10 Hz and twice that frequency, respectively, in the relaxed wakeful state are generated via subcortical feedback in this model, thereby leading to predictions of their frequencies in terms of physiological parameters, and of correlations in their occurrence. Subcortical feedback is also predicted to be responsible for production of anticorrelated peaks in deep sleep states that correspond to the occurrence of theta rhythm at around half the alpha frequency and sleep spindles at 3/2 times the alpha frequency. An additional positively correlated waking peak near three times the alpha frequency is also predicted and tentatively observed, as are two new types of sleep spindle near 5/2 and 7/2 times the alpha frequency, and anticorrelated with alpha. These results provide a theoretical basis for the conventional division of EEG spectra into frequency bands, but imply that the exact bounds of these bands depend on the individual. Three types of potential instability are found: one at zero frequency, another in the theta band at around half the alpha frequency, and a third at the alpha frequency itself.

Representationalism has defined the premise that cognition must involve a capacity to manipulate symbolic information. Although this approach has provided a good metaphoric and descriptive view of cognition, it ignores the distinct neural properties of the brain. This chapter has explored this problem by providing a more neurologically plausible account through the use of dynamical and chaotic systems theory. Symbols or representations were suggested to be epiphenomenonal to actual neural function and were considered as descriptions of behavior rather than cognition. Instead such entities were presumed to be embedded and decomposed in low level chaotic activity of the brain in such a manner that their localisation to specific neural entities was not a critical factor. The formation of knowledge, memories, or action was considered as an emergent property of distinct neural patterns of activity that result from the interaction of various neural groups.

We question the falsifiability of Tsuda's theory and emphasise the need for physiologically based, quantitative models of large scale cortical function that can be validated through experimental data. We outline such a model emphasising its verification through experimental data and possible avenues for testing Tsuda's predictions about nonlinearities in neural behaviour.

I question whether chaotic itinerancy is anything new or different to existing research on heteroclinic cycles (cycling-chaos), and blow-out bifurcations (attractor-bubbling) that provide more detailed and better definition for nonlinear phenomena occurring in neural systems. I give a brief description of this research for comparison and expansion, and see it as an important component in dynamical models of neural activity.